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 Thank You for Your Contribution in NICEDT-2024 and Invitation to NICEDT-2025Chandan.saroj.22.09 ek baat ayi hai mer khayaal me jhukti hain kyu palke teri mere sawaal me ake jawab de thoda sawaar le  mujhe saza de ya fir azaad kar  tujhe paane ke liye jo bhi kiya galat hai kiya hoon mai kya mujhko bata tere liye badal gaya tu mere liye  gusse dikha ke tu mujhko daraye  mujhe khudse milade  vo jo hai tu bhi mere jaisa  bhitar tere kyu usko chupaaye  phool maange phoolon ke kyari ko chokar titli chali sabse bade phool ko paane  kohre me koshis kai par itna tu jaan le tere jaise bhi pehle kai aye the paane par usko na koi fikar usko pata vo na koi phool hai usme nai koi khushboo hai  usko mai paane chala tha li thi udaan par mujhko na pata tha vo nahi vo uska ahsaas tha galat dishaaye mujhe laga pass hai tu bhari udaan aur paani me girkar de ye jaan in pankho se kaise naape brahmaand phool nahi vo asal me sawan ka channd hf_lfuCyicAdqQXdlfS...
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Stable Diffusion image pa

  import torch from diffusers import StableDiffusionPipeline , EulerDiscreteScheduler import matplotlib.pyplot as plt from PIL import Image # Load the pipeline pipe = StableDiffusionPipeline.from_pretrained ( "CompVis/stable-diffusion-v1-4" , torch_dtype=torch.float16 ) pipe = pipe.to ( "cuda" ) # Set a scheduler to include callback pipe.scheduler = EulerDiscreteScheduler.from_config ( pipe.scheduler.config ) # Define the callback to capture intermediate images intermediate_images = [] def save_intermediate_images ( pipeline , step , timestep , extra_inputs ) :     """     Callback function to save intermediate images.     """     latents = extra_inputs [ "latents" ]   # Retrieve the latents from the extra inputs     if step == 0 :         # Capture the first random latent (noise)         with torch.no_grad ():             random_latent_image = p...
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stable diffusion

  ! nvidia-smi ! pip uninstall -y diffusers huggingface_hub ! pip install diffusers transformers ftfy ipywidgets torch import torch from diffusers import StableDiffusionPipeline pipe = StableDiffusionPipeline.from_pretrained ( "CompVis/stable-diffusion-v1-4" , torch_dtype=torch.float16 )   pipe = pipe.to ( "cuda" ) prompt = "plane crash" image = pipe ( prompt ) .images [ 0 ]   # image here is in [PIL format](https://pillow.readthedocs.io/en/stable/) # Now to display an image you can either save it such as: image.save ( f "astronaut_rides_horse.png" ) # or if you're in a google colab you can directly display it with image import torch from diffusers import StableDiffusionPipeline , EulerDiscreteScheduler import matplotlib.pyplot as plt from PIL import Image # Load the pipeline pipe = StableDiffusionPipeline.from_pretrained ( "CompVis/stable-diffusion-v1-4" , torch_dtype=torch.float16 ) pipe = pipe.to ( "cuda"...
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 Pranjal@3152
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admin.py

# myapp/admin.py from django.utils.timezone import now from .models import UserProfile, UserReviews from django.utils import timezone from .models import Internship, Batch, AddTask, UserInternships, TaskSubmission from docx import Document from .Card.GenerateCard import write_text, write_qr import zipfile import pandas as pd import os from django.conf import settings from django.http import HttpResponse from django.contrib import admin, messages import qrcode from .models import UserInternships from PIL import Image from reportlab.lib.pagesizes import letter from reportlab.pdfgen import canvas import pypandoc import docx2pdf @admin.register(Internship) class InternshipAdmin(admin.ModelAdmin):     list_display = ('internship_id', 'domain', 'name', 'is_active')     search_fields = ('name', 'domain')     list_filter = ('is_active',) @admin.register(Batch) class BatchAdmin(admin.ModelAdmin):     list_display = ('internship', ...
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zip project on pythonanywhere.com

 zip -r myzipfile vishal
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captcha

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